Senior Data Scientist

Minimum Requirements:

• Matric (Grade 12)
• 5-7 years of experience manipulating data sets and building statistical models. 
• Having a degree in one of the following fields, Mathematics, Computer Science or another quantitative field is advantageous.
• Strong problem-solving skills with an emphasis on product development.
• Excellent written and verbal communication skills for coordinating across teams.
• Proven track record of implementing ML systems, using TensorFlow and
  PyTorch.
• Strong experience
• Using statistical computer languages (R, Python, SQL, etc.) to manipulate data and draw insights from large data sets.
• Querying databases/datasets
• Statistical and data mining techniques: GLM/Regression, Rando Forest, Boosting, Trees, text mining, social network analysis, etc.
• Creating and using advanced machine learning algorithms and statistics: regression, simulation, scenario analysis, modeling, clustering, decision trees, neural networks, etc.
• Visualizing/presenting data for stakeholders using: D3, ggplot, Kibana, Olik, Sisense etc.
• A variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages/drawbacks.
• Advanced statistical techniques and concepts (regression, properties of distributions, statistical tests and proper usage, etc.) and experience with applications.

Responsibilities:

• Work with stakeholders throughout the organization to identify
  opportunities for leveraging company data to drive business solutions.
• Mine and analyze data from company databases to drive optimization
  and improvement of product development, marketing techniques and
  business strategies.
• Assess the effectiveness and accuracy of new data sources and data
  gathering techniques.
• Develop custom data models and algorithms to apply to data sets.
• Use predictive modeling to increase and optimize customer experiences,
  revenue generation, ad targeting and other business outcomes.
• Develop company A/B testing framework and test model quality.
• Coordinate with different functional teams to implement models and
  monitor outcomes.
• Develop processes and tools to monitor and analyze model performance
  and data accuracy.